{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T23:12:00Z","timestamp":1778109120157,"version":"3.51.4"},"reference-count":65,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100013804","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003395","name":"Shanghai Municipal Education Commission","doi-asserted-by":"publisher","award":["SMEC-AI-DHUZ-05"],"award-info":[{"award-number":["SMEC-AI-DHUZ-05"]}],"id":[{"id":"10.13039\/501100003395","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006249","name":"Donghua University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006249","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176052"],"award-info":[{"award-number":["62176052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Digital Signal Processing"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.dsp.2026.106088","type":"journal-article","created":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T15:51:17Z","timestamp":1774194677000},"page":"106088","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Explicit knowledge-structured weakly supervised video anomaly detection"],"prefix":"10.1016","volume":"177","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3884-7493","authenticated-orcid":false,"given":"Chen","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7594-2241","authenticated-orcid":false,"given":"Xue-song","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9672-6161","authenticated-orcid":false,"given":"Kuangrong","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7174-3368","authenticated-orcid":false,"given":"Yubing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0007-9531","authenticated-orcid":false,"given":"Zhiqi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"6","key":"10.1016\/j.dsp.2026.106088_bib0001","doi-asserted-by":"crossref","first-page":"2813","DOI":"10.1109\/TCSVT.2022.3227716","article-title":"Boosting variational inference with margin learning for few-shot scene-adaptive anomaly detection","volume":"33","author":"Huang","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.dsp.2026.106088_bib0002","series-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"6848","article-title":"Real-time weakly supervised video anomaly detection","author":"Karim","year":"2024"},{"key":"10.1016\/j.dsp.2026.106088_bib0003","series-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"6479","article-title":"Real-world anomaly detection in surveillance videos","author":"Sultani","year":"2018"},{"key":"10.1016\/j.dsp.2026.106088_bib0004","series-title":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"16913","article-title":"Pixel-wise anomaly detection in complex driving scenes","author":"Di Biase","year":"2021"},{"key":"10.1016\/j.dsp.2026.106088_bib0005","series-title":"2019 IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"1705","article-title":"Memorizing normality to detect anomaly: memory-Augmented deep autoencoder for unsupervised anomaly detection","author":"Gong","year":"2019"},{"key":"10.1016\/j.dsp.2026.106088_bib0006","series-title":"Computer Vision \u2013 ECCV 2022","first-page":"729","article-title":"Self-supervised sparse representation for video anomaly detection","volume":"13673","author":"Wu","year":"2022"},{"key":"10.1016\/j.dsp.2026.106088_bib0007","series-title":"2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"16271","article-title":"Exploiting completeness and uncertainty of pseudo labels for weakly supervised video anomaly detection","author":"Zhang","year":"2023"},{"key":"10.1016\/j.dsp.2026.106088_bib0008","series-title":"2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"14724","article-title":"Generative cooperative learning for unsupervised video anomaly detection","author":"Zaheer","year":"2022"},{"key":"10.1016\/j.dsp.2026.106088_bib0009","series-title":"2021 IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"4955","article-title":"Weakly-supervised video anomaly detection with robust temporal feature magnitude learning","author":"Tian","year":"2021"},{"key":"10.1016\/j.dsp.2026.106088_bib0010","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"3769","article-title":"Dual memory units with uncertainty regulation for weakly supervised video anomaly detection","volume":"37","author":"Zhou","year":"2023"},{"key":"10.1016\/j.dsp.2026.106088_bib0011","series-title":"2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"12137","article-title":"Look around for anomalies: weakly-supervised anomaly detection via context-motion relational learning","author":"Cho","year":"2023"},{"key":"10.1016\/j.dsp.2026.106088_bib0012","unstructured":"J. Wu, W. Zhang, G. Li, W. Wu, X. Tan, Y. Li, E. Ding, L. Lin, Weakly-supervised spatio-temporal anomaly detection in surveillance video, 2021, arXiv: 2108.0382510.48550\/arXiv.2108.03825."},{"key":"10.1016\/j.dsp.2026.106088_bib0013","series-title":"Proceedings of the 30th ACM International Conference on Multimedia","first-page":"6278","article-title":"Modality-aware contrastive instance learning with self-distillation for weakly-supervised audio-visual violence detection","author":"Yu","year":"2022"},{"key":"10.1016\/j.dsp.2026.106088_bib0014","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"5549","article-title":"TEVAD: improved video anomaly detection with captions","author":"Chen","year":"2023"},{"key":"10.1016\/j.dsp.2026.106088_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2025.105076","article-title":"Spatial scene temporal behavior framework for anomaly detection","volume":"160","author":"Li","year":"2025","journal-title":"Digit Signal Process."},{"issue":"11","key":"10.1016\/j.dsp.2026.106088_bib0016","doi-asserted-by":"crossref","first-page":"2109","DOI":"10.1109\/TCSVT.2015.2492838","article-title":"Video anomaly detection in real time on a power-aware heterogeneous platform","volume":"26","author":"Blair","year":"2016","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.dsp.2026.106088_bib0017","series-title":"2023 IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"5504","article-title":"Feature prediction diffusion model for video anomaly detection","author":"Yan","year":"2023"},{"issue":"12","key":"10.1016\/j.dsp.2026.106088_bib0018","doi-asserted-by":"crossref","first-page":"4639","DOI":"10.1109\/TCSVT.2019.2962229","article-title":"Attention-driven loss for anomaly detection in video surveillance","volume":"30","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.dsp.2026.106088_bib0019","series-title":"2021 IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"13568","article-title":"A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction","author":"Liu","year":"2021"},{"key":"10.1016\/j.dsp.2026.106088_bib0020","doi-asserted-by":"crossref","first-page":"8429","DOI":"10.1109\/TIP.2020.3013168","article-title":"Unsupervised learning of optical flow with CNN-Based non-local filtering","volume":"29","author":"Tian","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.dsp.2026.106088_bib0021","series-title":"Computer Vision \u2013 ECCV 2020","first-page":"329","article-title":"Clustering driven deep autoencoder for video anomaly detection","volume":"12360","author":"Chang","year":"2020"},{"key":"10.1016\/j.dsp.2026.106088_bib0022","series-title":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"12737","article-title":"Anomaly detection in video via self-supervised and multi-task learning","author":"Georgescu","year":"2021"},{"key":"10.1016\/j.dsp.2026.106088_bib0023","series-title":"2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2893","article-title":"OCGAN: one-class novelty detection using GANs with constrained latent representations","author":"Perera","year":"2019"},{"key":"10.1016\/j.dsp.2026.106088_bib0024","series-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","first-page":"1025","article-title":"Continual learning for anomaly detection in surveillance videos","author":"Doshi","year":"2020"},{"key":"10.1016\/j.dsp.2026.106088_bib0025","series-title":"2025 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS)","first-page":"1","article-title":"Generative adversarial learning for anomaly detection in high-volume data streams over fog computing layers","author":"Chandrappa","year":"2025"},{"key":"10.1016\/j.dsp.2026.106088_bib0026","series-title":"The Thirteenth International Conference on Learning Representations","article-title":"Local patterns generalize better for novel anomalies","author":"Jiang","year":"2025"},{"key":"10.1016\/j.dsp.2026.106088_bib0027","series-title":"Computer Vision \u2013 ECCV 2022","first-page":"395","article-title":"Towards open set video anomaly detection","volume":"13694","author":"Zhu","year":"2022"},{"key":"10.1016\/j.dsp.2026.106088_bib0028","article-title":"VadCLIP++: dynamic vision-language model for weakly supervised video anomaly detection","volume":"172, Part C","author":"Liu","year":"2025","journal-title":"Digit. Signal Process."},{"key":"10.1016\/j.dsp.2026.106088_bib0029","first-page":"89943","article-title":"Advancing video anomaly detection: a concise review and a new dataset","volume":"37","author":"Zhu","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.dsp.2026.106088_bib0030","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"8022","article-title":"Unbiased multiple instance learning for weakly supervised video anomaly detection","author":"Lv","year":"2023"},{"issue":"5","key":"10.1016\/j.dsp.2026.106088_bib0031","doi-asserted-by":"crossref","first-page":"3197","DOI":"10.1109\/TCYB.2022.3227044","article-title":"Weakly supervised video anomaly detection via self-guided temporal discriminative transformer","volume":"54","author":"Huang","year":"2024","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.dsp.2026.106088_bib0032","series-title":"Proceedings of the Computer Vision and Pattern Recognition Conference","first-page":"24265","article-title":"Just dance with pi! A poly-modal inductor for weakly-supervised video anomaly detection","author":"Majhi","year":"2025"},{"issue":"7","key":"10.1016\/j.dsp.2026.106088_bib0033","doi-asserted-by":"crossref","first-page":"5480","DOI":"10.1109\/TCSVT.2024.3350084","article-title":"Weakly-supervised video anomaly detection with snippet anomalous attention","volume":"34","author":"Fan","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.dsp.2026.106088_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2024.105169","article-title":"Event-driven weakly supervised video anomaly detection","volume":"149","author":"Sun","year":"2024","journal-title":"Image Vis. Comput."},{"key":"10.1016\/j.dsp.2026.106088_bib0035","unstructured":"Y. Wang, S. Chen, Learning event completeness for weakly supervised video anomaly detection, arXiv preprint arXiv: 2506.13095(2025)."},{"key":"10.1016\/j.dsp.2026.106088_bib0036","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"1237","article-title":"Graph convolutional label noise cleaner: train a plug-and-play action classifier for anomaly detection","author":"Zhong","year":"2019"},{"key":"10.1016\/j.dsp.2026.106088_bib0037","series-title":"Computer Vision \u2013 ECCV 2020","first-page":"358","article-title":"CLAWS: clustering assisted weakly supervised learning with normalcy suppression for anomalous event detection","volume":"12367","author":"Zaheer","year":"2020"},{"issue":"9","key":"10.1016\/j.dsp.2026.106088_bib0038","first-page":"1","article-title":"Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing","volume":"55","author":"Liu","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.dsp.2026.106088_bib0039","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.dsp.2026.106088_bib0040","series-title":"International Conference on Machine Learning","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"key":"10.1016\/j.dsp.2026.106088_bib0041","series-title":"International Conference on Machine Learning","first-page":"4904","article-title":"Scaling up visual and vision-language representation learning with noisy text supervision","author":"Jia","year":"2021"},{"key":"10.1016\/j.dsp.2026.106088_bib0042","series-title":"Computer Vision \u2013 ECCV 2022","first-page":"709","article-title":"Visual prompt tuning","volume":"13693","author":"Jia","year":"2022"},{"key":"10.1016\/j.dsp.2026.106088_bib0043","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"6074","article-title":"Vadclip: adapting vision-language models for weakly supervised video anomaly detection","volume":"38","author":"Wu","year":"2024"},{"key":"10.1016\/j.dsp.2026.106088_bib0044","doi-asserted-by":"crossref","first-page":"2056","DOI":"10.1109\/TMM.2023.3291588","article-title":"Dual modality prompt tuning for vision-language pre-trained model","volume":"26","author":"Xing","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.dsp.2026.106088_bib0045","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"18899","article-title":"Text prompt with normality guidance for weakly supervised video anomaly detection","author":"Yang","year":"2024"},{"key":"10.1016\/j.dsp.2026.106088_bib0046","series-title":"European Conference on Computer Vision","first-page":"234","article-title":"Fedvad: enhancing federated video anomaly detection with gpt-driven semantic distillation","author":"Qi","year":"2024"},{"key":"10.1016\/j.dsp.2026.106088_bib0047","series-title":"Proceedings of the 32nd ACM International Conference on Multimedia","first-page":"9301","article-title":"Weakly supervised video anomaly detection and localization with spatio-temporal prompts","author":"Wu","year":"2024"},{"key":"10.1016\/j.dsp.2026.106088_bib0048","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"21017","article-title":"Federated weakly supervised video anomaly detection with multimodal prompt","volume":"39","author":"Wang","year":"2025"},{"key":"10.1016\/j.dsp.2026.106088_bib0049","series-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"4724","article-title":"Quo vadis, action recognition? a new model and the kinetics dataset","author":"Carreira","year":"2017"},{"issue":"1","key":"10.1016\/j.dsp.2026.106088_bib0050","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1038\/s41593-023-01510-5","article-title":"The logic of recurrent circuits in the primary visual cortex","volume":"27","author":"Oldenburg","year":"2024","journal-title":"Nat. Neurosci."},{"issue":"1","key":"10.1016\/j.dsp.2026.106088_bib0051","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1146\/annurev-neuro-072116-031418","article-title":"Circuits and mechanisms for surround modulation in visual cortex","volume":"40","author":"Angelucci","year":"2017","journal-title":"Annu. Rev. Neurosci."},{"key":"10.1016\/j.dsp.2026.106088_bib0052","series-title":"Computer Vision \u2013 ECCV 2020","first-page":"322","article-title":"Not only look, but also listen: learning multimodal violence detection under weak supervision","volume":"12375","author":"Wu","year":"2020"},{"key":"10.1016\/j.dsp.2026.106088_bib0053","series-title":"Computer Vision \u2013 ECCV 2024","first-page":"163","article-title":"Learning anomalies with normality prior for unsupervised video anomaly detection","volume":"15064","author":"Shi","year":"2025"},{"key":"10.1016\/j.dsp.2026.106088_bib0054","series-title":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"14004","article-title":"MIST: multiple instance self-training framework for video anomaly detection","author":"Feng","year":"2021"},{"key":"10.1016\/j.dsp.2026.106088_bib0055","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"1395","article-title":"Self-training multi-sequence learning with transformer for weakly supervised video anomaly detection","volume":"36","author":"Li","year":"2022"},{"key":"10.1016\/j.dsp.2026.106088_bib0056","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.110119","article-title":"Adversarial and focused training of abnormal videos for weakly-supervised anomaly detection","volume":"147","author":"He","year":"2024","journal-title":"Pattern Recognit."},{"issue":"2","key":"10.1016\/j.dsp.2026.106088_bib0057","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.1109\/TCSVT.2024.3482414","article-title":"Inter-clip feature similarity based weakly supervised video anomaly detection via multi-Scale temporal MLP","volume":"35","author":"Zhong","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"7","key":"10.1016\/j.dsp.2026.106088_bib0058","doi-asserted-by":"crossref","first-page":"5480","DOI":"10.1109\/TCSVT.2024.3350084","article-title":"Weakly-supervised video anomaly detection with snippet anomalous attention","volume":"34","author":"Fan","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.dsp.2026.106088_bib0059","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"18297","article-title":"Open-vocabulary video anomaly detection","author":"Wu","year":"2024"},{"key":"10.1016\/j.dsp.2026.106088_bib0060","series-title":"2025 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)","first-page":"9587","article-title":"Discriminative score suppression for weakly supervised video anomaly detection","author":"Xu","year":"2025"},{"issue":"5","key":"10.1016\/j.dsp.2026.106088_bib0061","doi-asserted-by":"crossref","first-page":"4135","DOI":"10.1109\/TCSVT.2023.3321235","article-title":"Towards video anomaly detection in the real world: a binarization embedded weakly-supervised network","volume":"34","author":"Yang","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.dsp.2026.106088_bib0062","article-title":"DSCIL: dynamic selected contrastive instance learning for weakly supervised video anomaly detection","volume":"172, Part C","author":"Zeng","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.dsp.2026.106088_bib0063","unstructured":"Q. Zhou, G. Pang, Y. Tian, S. He, J. Chen, AnomalyCLIP: object-agnostic prompt learning for zero-shot anomaly detectionThe Twelfth International Conference on Learning Representations(2023)."},{"key":"10.1016\/j.dsp.2026.106088_bib0064","series-title":"2021 IEEE International Conference on Image Processing (ICIP)","first-page":"1114","article-title":"Multi-scale background suppression anomaly detection in surveillance videos","author":"Zhen","year":"2021"},{"key":"10.1016\/j.dsp.2026.106088_bib0065","series-title":"2022 IEEE International Conference on Multimedia and Expo (ICME)","first-page":"1","article-title":"Locality-aware attention network with discriminative dynamics learning for weakly supervised anomaly detection","author":"Pu","year":"2022"}],"container-title":["Digital Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1051200426002071?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1051200426002071?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T22:27:42Z","timestamp":1778106462000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1051200426002071"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":65,"alternative-id":["S1051200426002071"],"URL":"https:\/\/doi.org\/10.1016\/j.dsp.2026.106088","relation":{},"ISSN":["1051-2004"],"issn-type":[{"value":"1051-2004","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Explicit knowledge-structured weakly supervised video anomaly detection","name":"articletitle","label":"Article Title"},{"value":"Digital Signal Processing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.dsp.2026.106088","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"106088"}}